Most existing image restoration methods can only restore a specific image to a fixed result.
Controllable image restoration methods aim to solve this problem by allowing users to adjust the restoration strength. Some pioneer works (e.g., DNI [1]} and AdaFM [2]}) find that the learned filters of restoration models trained with different restoration levels are pretty similar at visual patterns. Based on this observation, DNI directly interpolated kernels to attain a smooth control of diverse imagery effects. AdaFM adopts a more efficient way by inserting AdaFM layers to change the statistics of filters.
The work in CFSNet [3]} adaptively learns the interpolation coefficients and uses them to couple intermediate features from the main branch and tuning branch. Recently, CResMD [4]} distinguished different degradation types in the modulation process and proposed a framework that accepts both corrupted images and their degradation information as input to realize multi-dimension modulation.
Following this strategy, [5]} proposed a GAN-based image restoration framework to produce higher perceptual quality with interactive modulation. However, existing modulation strategies can only be trained in a supervised manner to deal with simple and first-order degradations, which have limitations in real-world applications.
